Each sentence in \(\mathcal {D}\)  is represented as a feature vector. A classical approach is to use the BOW (Bag-Of-Words) representation, where each element in the vector corresponds to a topic word in a pre-defined vocabulary, and the value of the element is the weight of the topic word assigned using a scoring model, such as TF-IDF, BM25, log-likelihood ratio, and so on.
The feature vector can be enriched by incorporating indicative information such as sentence length and position in the document.
In neural approaches, the feature vector is produced using sentence encoders implemented using various neural network models.
For example, [1]} employ a recurrent neural network (RNN) or a convolutional neural network (CNN) to encode the words in a sentence to obtain a vector representation of the sentence.
[2]} utilize pre-trained language models (e.g., BERT) to encode sentences.
A recent survey on this topic is [3]}.
